Dear Abdul,

84 "samples" is a good number. I propose you to divide the dataset in two (64/20). Try to select representative samples in the two datasets. Develop a PLS calibration model with the 64 samples. Often we use cross-validation for this calibration. The 20 left out samples are finally used for prediction in order to estimate accuracy. If you need help, don't hesitate to contact me.

Ludovic.

84 respondents is good enough for Pls-SEM using Smart-pls software. However, the study deemed to be exploratory instead of confirmatory.

Dear Abdul,

84 "samples" is a good number. I propose you to divide the dataset in two (64/20). Try to select representative samples in the two datasets. Develop a PLS calibration model with the 64 samples. Often we use cross-validation for this calibration. The 20 left out samples are finally used for prediction in order to estimate accuracy. If you need help, don't hesitate to contact me.

Ludovic.

Dear Ludovic,

Thanks a lot for your guidance. I will contact you, if i need help. 

Thanks again. 

What is the minimum sample size required for Partial Least Squares (PLS) analysis?

You can refer to my answer to similar question in this RG link.  SmartPLS is one of the software tools to perform PLS analysis.

https://www.researchgate.net/post/What_is_the_minimum_sample_acceptable_for_structural_equation_modelling_using_smartPLS/1

PLS is considered better suited for explaining complex relationships. "PLS comes to the fore in larger models, when the importance shifts from individual variables and parameters to packages of variables and aggregate parameters."

Sample size can be smaller, with a strong rule of thumb suggesting that it be equal to the larger of the following:

(1) ten times the scale with the largest number of formative (i.e., causal) indicators (note that scales for constructs designated with reflective indicators can be ignored), or

(2) ten times the largest number of structural paths directed at a particular construct in the structural model. A weak rule of thumb, similar to the heuristic for multiple regressions would be to use a multiplier of five instead of ten for the preceding formulae. Second order factors can be approximated using various procedures. One of the easiest to implement is the approach of repeated indicators known as the hierarchical component model. In essence, a second order factor is directly measured by observed variables for all the first order factors. While this approach repeats the number of manifest variables used, the model can be estimated by the standard PLS algorithm. This procedure works best with equal numbers of indicators for each construct.

Thanks a lot.